Series indexing and selection in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of selecting elements with mixed index types

What is the output of this code snippet?

Data Analysis Python

import pandas as pd
s = pd.Series([10, 20, 30, 40], index=['a', 1, 'b', 2])
result = s.loc[[1, 'b']]
print(result)

A KeyError: '[1, 'b'] not found in axis'

B

a     10
b     30
dtype: int64

C

1    20
2    40
dtype: int64

D

1     20
b     30
dtype: int64

💡 Hint

Remember that .loc uses label-based indexing and can handle mixed types in the index.

The Series has mixed index labels: 'a', 1, 'b', 2. Using .loc with [1, 'b'] selects the elements with labels 1 and 'b', which correspond to values 20 and 30.

❓ data_output

intermediate

Number of elements selected by slicing with .iloc

How many elements are selected by this code?

Data Analysis Python

import pandas as pd
s = pd.Series(range(10), index=list('abcdefghij'))
subset = s.iloc[3:7]
print(len(subset))

A 3

B 4

C 5

D 7

💡 Hint

Remember that slicing with iloc is end-exclusive.

The slice 3:7 selects elements at positions 3,4,5,6, which is 4 elements.

🔧 Debug

advanced

Identify the error in this Series selection code

What error does this code raise?

Data Analysis Python

import pandas as pd
s = pd.Series([1,2,3], index=[0,1,2])
result = s.loc[1:3]
print(result)

A No error, outputs elements with labels 1 and 2

B IndexError: index out of range

C TypeError: unhashable type

D KeyError: 3

💡 Hint

.loc slicing with labels is inclusive of the stop label.

Using .loc with slice 1:3 selects labels 1 and 2 because label 3 does not exist, but slicing stops at the last valid label before 3.

🧠 Conceptual

advanced

Understanding difference between .loc and .iloc with duplicate indices

Given a Series with duplicate indices, which statement is true about .loc and .iloc?

A .loc returns all rows matching the label, .iloc returns rows by position regardless of duplicates

B .loc returns only the first matching label, .iloc returns all positions of that label

C .loc and .iloc behave identically with duplicate indices

D .loc raises an error if duplicates exist, .iloc does not

💡 Hint

Think about label-based vs position-based selection.

.loc selects all rows with the given label, even if duplicated. .iloc selects rows by integer position, ignoring labels.

🚀 Application

expert

Select elements conditionally using Series indexing

Given the Series below, which code snippet correctly selects all elements with values greater than 15 and index labels that are strings?

Data Analysis Python

import pandas as pd
s = pd.Series([10, 20, 30, 40], index=['a', 1, 'b', 2])

A s[(s > 15) & (s.index.map(type) == str)]

B s.loc[(s > 15) & (s.index.map(type) == str)]

C s[(s > 15) & (s.index.map(lambda x: isinstance(x, str)))]

D s.loc[(s > 15) & (s.index.map(lambda x: isinstance(x, str)))]

💡 Hint

Use .map with a function to check index types and combine conditions properly.

Option C correctly uses .map with a lambda to check if index labels are strings and combines it with the value condition. Options A and C compare arrays incorrectly, and D uses .loc with a boolean index which is valid but the condition in D is same as B but .loc is not needed here.